1 Introduction

This paper contains estimates for the effective reproduction number \(R_{t,m}\) over time \(t\) in various countries \(m\) of the world. This is done using the methodology as described in [1]. These have been implemented in R using EpiEstim package [2] which is what is used here. The methodology and assumptions are described in more detail here.

This paper and it’s results should be updated roughly daily and is available online.

As this paper is updated over time this section will summarise significant changes. The code producing this paper is tracked using Git. The Git commit hash for this project at the time of generating this paper was 3a5554873d47a90556cbb9b5bad9159dce6322d0.

2 Data

Data are downloaded from [3]. Minor formatting is applied to get the data ready for further processing.

3 Basic Exploration

Below we plot cumulative case count on a log scale by continent. Note that “Other” relates to ships.

Reported Cases by Continent

Reported Cases by Continent

Below we plot the cumulative deaths by country on a log scale:

Reported Deaths by Continent

Reported Deaths by Continent

4 Method & Assumptions

The methodology is described in detail here.

Countries with populations of less than 500 000 are excluded.

5 Results

5.1 World-wide

Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
cases 2,696,769 2021-03-06 1 1 1
deaths 61,601 2021-03-06 1 1 1

5.2 Current reproduction number estimates by country

Below current (last weekly) \(R_{t,m}\) estimates are plotted on a world map.

5.2.0.1 Cases

5.2.1 Deaths

5.3 Top 10 countries

Below we show various extremes of \(R_{t,m}\) where counts (deaths or cases) exceed 50 in the last week.

5.3.1 Lowest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Nepal deaths 237 2021-03-06 0.3 0.4 0.6
Ireland deaths 106 2021-03-06 0.5 0.7 0.8
Philippines deaths 176 2021-03-06 0.5 0.7 0.8
Portugal deaths 236 2021-03-06 0.6 0.7 0.8
Honduras deaths 106 2021-03-06 0.6 0.7 0.9
Indonesia deaths 1,173 2021-03-06 0.7 0.8 0.8
Israel deaths 124 2021-03-06 0.6 0.8 0.9
Libya deaths 62 2021-03-06 0.6 0.8 1.0
United Kingdom deaths 1,715 2021-03-06 0.7 0.8 0.8
South Africa deaths 706 2021-03-06 0.7 0.8 0.8

5.3.2 Lowest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Guinea-Bissau cases 65 2021-03-06 0.4 0.5 0.6
Gambia cases 68 2021-03-06 0.4 0.5 0.7
Malawi cases 600 2021-03-06 0.5 0.5 0.6
Haiti cases 106 2021-03-06 0.5 0.6 0.8
Honduras cases 3,266 2021-03-06 0.6 0.7 0.7
Burundi cases 118 2021-03-06 0.5 0.7 0.8
Thailand cases 354 2021-03-06 0.6 0.7 0.8
Eritrea cases 97 2021-03-06 0.6 0.7 0.9
Spain cases 34,806 2021-03-06 0.7 0.7 0.8
Peru cases 34,431 2021-03-06 0.7 0.7 0.8

5.3.3 Highest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Bosnia and Herzegovina deaths 157 2021-03-06 1.3 1.6 2.0
Somalia deaths 63 2021-03-06 1.2 1.6 2.0
Palestine deaths 102 2021-03-06 1.2 1.5 1.8
Jordan deaths 225 2021-03-06 1.2 1.4 1.7
Hungary deaths 863 2021-03-06 1.1 1.2 1.3
Pakistan deaths 345 2021-03-06 1.1 1.2 1.3
Argentina deaths 924 2021-03-06 1.1 1.2 1.3
Iraq deaths 165 2021-03-06 1.0 1.2 1.4
Czechia deaths 1,364 2021-03-06 1.1 1.2 1.2
Brazil deaths 10,104 2021-03-06 1.1 1.2 1.2

5.3.4 Highest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Madagascar cases 324 2021-03-06 3.8 7.0 17.6
Benin cases 637 2021-03-06 1.7 3.5 8.6
Cote d’Ivoire cases 2,304 2021-03-06 2.1 2.7 3.1
Mali cases 195 2021-03-06 1.8 2.1 2.5
Chile cases 55,477 2021-03-06 2.1 2.1 2.1
Congo cases 359 2021-03-06 1.4 1.6 2.0
Kosovo cases 4,149 2021-03-06 1.4 1.5 1.7
Djibouti cases 69 2021-03-06 1.1 1.5 1.8
Kenya cases 2,714 2021-03-06 1.4 1.5 1.6
Armenia cases 2,464 2021-03-06 1.3 1.4 1.5

5.4 Risk Quadrants

The plots below show weekly cases (or deaths) on the X-axis and the reproduction number on the Y-axis. By dividing this into 4 quadrants we can identify countries with high cases and high reproduction numbers, or high cases and low reproduction numbers etc.

Values where the reproduction number exceeds 3 are plotted at 3.

5.4.1 Cases

Risk Quadrants - Cases

5.4.2 Deaths

Risk Quadrants - Deaths

5.5 Country Plots by Continent

Below we plot results for each country/province in a list. Values larger than 3 are plotted at 3.

5.5.1 Africa

5.5.1.1 Algeria

5.5.1.2 Angola

5.5.1.3 Benin

5.5.1.4 Botswana

5.5.1.5 Burkina Faso

5.5.1.6 Burundi

5.5.1.7 Cameroon

5.5.1.8 Cape Verde

5.5.1.9 Central African Republic

5.5.1.10 Chad

5.5.1.11 Comoros

5.5.1.12 Congo

5.5.1.13 Cote d’Ivoire

5.5.1.14 Democratic Republic of Congo

5.5.1.15 Djibouti

5.5.1.16 Egypt

5.5.1.17 Equatorial Guinea

5.5.1.18 Eritrea

5.5.1.19 Eswatini

5.5.1.20 Ethiopia

5.5.1.21 Gabon

5.5.1.22 Gambia

5.5.1.23 Ghana

5.5.1.24 Guinea

5.5.1.25 Guinea-Bissau

5.5.1.26 Kenya

5.5.1.27 Lesotho

5.5.1.28 Liberia

5.5.1.29 Libya

5.5.1.30 Madagascar

5.5.1.31 Malawi

5.5.1.32 Mali

5.5.1.33 Mauritania

5.5.1.34 Mauritius

5.5.1.35 Morocco

5.5.1.36 Mozambique

5.5.1.37 Namibia

5.5.1.38 Niger

5.5.1.39 Nigeria

5.5.1.40 Rwanda

5.5.1.41 Senegal

5.5.1.42 Sierra Leone

5.5.1.43 Somalia

5.5.1.44 South Africa

5.5.1.45 South Sudan

5.5.1.46 Sudan

5.5.1.47 Togo

5.5.1.48 Tunisia

5.5.1.49 Uganda

5.5.1.50 Zambia

5.5.1.51 Zimbabwe

5.5.2 Asia

5.5.2.1 Afghanistan

5.5.2.2 Armenia

5.5.2.3 Azerbaijan

5.5.2.4 Bahrain

5.5.2.5 Bangladesh

5.5.2.6 Bhutan

5.5.2.7 Cambodia

5.5.2.8 China

5.5.2.9 Georgia

5.5.2.10 India

5.5.2.11 Indonesia

5.5.2.12 Iran

5.5.2.13 Iraq

5.5.2.14 Israel

5.5.2.15 Japan

5.5.2.16 Jordan

5.5.2.17 Kazakhstan

5.5.2.18 Kuwait

5.5.2.19 Kyrgyzstan

5.5.2.20 Lebanon

5.5.2.21 Malaysia

5.5.2.22 Maldives

5.5.2.23 Mongolia

5.5.2.24 Myanmar

5.5.2.25 Nepal

5.5.2.26 Oman

5.5.2.27 Pakistan

5.5.2.28 Palestine

5.5.2.29 Philippines

5.5.2.30 Qatar

5.5.2.31 Saudi Arabia

5.5.2.32 Singapore

5.5.2.33 South Korea

5.5.2.34 Sri Lanka

5.5.2.35 Syria

5.5.2.36 Taiwan

5.5.2.37 Tajikistan

5.5.2.38 Thailand

5.5.2.39 Turkey

5.5.2.40 United Arab Emirates

5.5.2.41 Uzbekistan

5.5.2.42 Vietnam

5.5.2.43 Yemen

5.5.3 Europe

5.5.3.1 Albania

5.5.3.2 Austria

5.5.3.3 Belarus

5.5.3.4 Belgium

5.5.3.5 Bosnia and Herzegovina

5.5.3.6 Bulgaria

5.5.3.7 Croatia

5.5.3.8 Cyprus

5.5.3.9 Czechia

5.5.3.10 Denmark

5.5.3.11 Estonia

5.5.3.12 Finland

5.5.3.13 France

5.5.3.14 Germany

5.5.3.15 Greece

5.5.3.16 Hungary

5.5.3.17 Ireland

5.5.3.18 Italy

5.5.3.19 Kosovo

5.5.3.20 Latvia

5.5.3.21 Lithuania

5.5.3.22 Luxembourg

5.5.3.23 Moldova

5.5.3.24 Montenegro

5.5.3.25 Netherlands

5.5.3.26 North Macedonia

5.5.3.27 Norway

5.5.3.28 Poland

5.5.3.29 Portugal

5.5.3.30 Romania

5.5.3.31 Russia

5.5.3.32 Serbia

5.5.3.33 Slovakia

5.5.3.34 Slovenia

5.5.3.35 Spain

5.5.3.36 Sweden

5.5.3.37 Switzerland

5.5.3.38 Ukraine

5.5.3.39 United Kingdom

5.5.4 North America

5.5.4.1 Canada

5.5.4.2 Costa Rica

5.5.4.3 Cuba

5.5.4.4 Dominican Republic

5.5.4.5 El Salvador

5.5.4.6 Guatemala

5.5.4.7 Haiti

5.5.4.8 Honduras

5.5.4.9 Jamaica

5.5.4.10 Mexico

5.5.4.11 Nicaragua

5.5.4.12 Panama

5.5.4.13 Trinidad and Tobago

5.5.4.14 United States

5.5.5 Oceania

5.5.5.1 Australia

5.5.5.2 New Zealand

5.5.5.3 Papua New Guinea

5.5.6 South America

5.5.6.1 Argentina

5.5.6.2 Bolivia

5.5.6.3 Brazil

5.5.6.4 Chile

5.5.6.5 Colombia

5.5.6.6 Ecuador

5.5.6.7 Guyana

5.5.6.8 Paraguay

5.5.6.9 Peru

5.5.6.10 Suriname

5.5.6.11 Uruguay

5.5.6.12 Venezuela

5.6 Detailed Output

Detailed output for all countries are saved to a comma-separated value file. The file can be found here.

6 Discussion

Limitation of this method to estimate \(R_{t,m}\) are noted in [1]

  • It’s sensitive to changes in transmissibility, changes in contact patterns, depletion of the susceptible population and control measures.
  • It relies on an assumed generation interval assumptions.
  • The size of the time window can affect the volatility of results.
  • Results are time lagged with regards to true infection, more so in the case of the use of deaths.
  • It’s sensitive to changes in case (or death) detection.
  • The generation interval may change over time.

Further to the above the estimates are made under assumption that the cases and deaths are reported consistently over time. For cases this means that testing needs to be at similar levels and reported with similar lag. Should these change rapidly over an interval of a few weeks the above estimates of the effective reproduction numbers would be biased. For example a rapid expansion of testing over the last 3 weeks would results in overestimating recent effective reproduction numbers. Similarly any changes in reporting (over time and underreporting) of deaths would also bias estimates of the reproduction number estimated using deaths.

Estimates for the reproduction number are plotted in time period in which the relevant measure is recorded. Though in reality the infections giving rise to those estimates would have occurred roughly between a week to 4 weeks earlier depending on whether it was cases or deaths. These figures have not been shifted back.

Despite these limitation we believe the ease of calculation of this method and the ability to use multiple sources makes it useful as a monitoring tool.

7 Author

This report was prepared by Louis Rossouw. Please get in contact with Louis Rossouw if you have comments or wish to receive this regularly.

Louis Rossouw
Head of Research & Analytics
Gen Re | Life/Health Canada, South Africa, Australia, NZ, UK & Ireland
Email: LRossouw@GenRe.com Mobile: +27 71 355 2550

The views in this document represents that of the author and may not represent those of Gen Re. Also note that given the significant uncertainty involved with the parameters, data and methodology care should be taken with these numbers and any use of these numbers.

References

[1] A. Cori, N. M. Ferguson, C. Fraser, and S. Cauchemez, “A new framework and software to estimate time-varying reproduction numbers during epidemics,” American Journal of Epidemiology, vol. 178, no. 9, pp. 1505–1512, Sep. 2013, doi: 10.1093/aje/kwt133. [Online]. Available: https://doi.org/10.1093/aje/kwt133

[2] A. Cori, EpiEstim: A package to estimate time varying reproduction numbers from epidemic curves. 2013 [Online]. Available: https://CRAN.R-project.org/package=EpiEstim

[3] M. Roser, H. Ritchie, E. Ortiz-Ospina, and J. Hasell, “Coronavirus pandemic (COVID-19),” Our World in Data, 2020 [Online]. Available: https://ourworldindata.org/coronavirus. [Accessed: 17-Dec-2020]